Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models
Summary This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive...
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| Vydáno v: | International journal for numerical methods in engineering Ročník 123; číslo 13; s. 2986 - 3003 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
15.07.2022
Wiley Subscription Services, Inc |
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| ISSN: | 0029-5981, 1097-0207 |
| On-line přístup: | Získat plný text |
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| Abstract | Summary
This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. |
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| AbstractList | Summary
This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. |
| Author | Tran, M. Willcox, K.E. Kapteyn, M.G. Huynh, D.B.P. Knezevic, D.J. |
| Author_xml | – sequence: 1 givenname: M.G. orcidid: 0000-0002-3594-682X surname: Kapteyn fullname: Kapteyn, M.G. email: mkapteyn@mit.edu organization: Massachusetts Institute of Technology – sequence: 2 givenname: D.J. surname: Knezevic fullname: Knezevic, D.J. organization: Akselos Inc – sequence: 3 givenname: D.B.P. surname: Huynh fullname: Huynh, D.B.P. organization: Akselos S.A – sequence: 4 givenname: M. surname: Tran fullname: Tran, M. organization: Akselos S.A – sequence: 5 givenname: K.E. surname: Willcox fullname: Willcox, K.E. organization: University of Texas at Austin |
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This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create... This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven... |
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| SubjectTerms | Adaptation Aircraft Aircraft components Aircraft structures Bayesian analysis Complex systems data‐model fusion digital twin Digital twins Libraries Model reduction model updating Physics reduced‐order model State estimation Structural damage unmanned aerial vehicle Unmanned aerial vehicles Wing span |
| Title | Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models |
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